Article: Object detection for construction site safety monitoring based on Yolov8 model Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.12 pp.66 - 82 Abstract: Accidents frequently occur at construction sites, and traditional video surveillance relies on manual inspections, which have problems of delayed response and high false alarm rate. This paper addresses the challenge of precise identification of small and occluded targets in complex environments and proposes a real-time detection framework based on the improved you only look once version 8. By integrating multi-scale features and optimising the network structure, the system achieves an average precision of 92.3% on the public safety helmet dataset, which is 8.5 percentage points higher than the benchmark model you only look once version v5; the processing speed on edge devices reaches 45 frames per second, meeting the requirements of real-time monitoring. This method enables automatic identification and warning of behaviours such as wearing safety equipment and intrusion into dangerous areas, providing effective technical support for building an intelligent safety defence line. Inderscience Publishers - linking academia, business and industry through research

Title: Object detection for construction site safety monitoring based on Yolov8 model

Authors: Mingyue Qu; Jingjing Zheng

Addresses: College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China ' College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China

Abstract: Accidents frequently occur at construction sites, and traditional video surveillance relies on manual inspections, which have problems of delayed response and high false alarm rate. This paper addresses the challenge of precise identification of small and occluded targets in complex environments and proposes a real-time detection framework based on the improved you only look once version 8. By integrating multi-scale features and optimising the network structure, the system achieves an average precision of 92.3% on the public safety helmet dataset, which is 8.5 percentage points higher than the benchmark model you only look once version v5; the processing speed on edge devices reaches 45 frames per second, meeting the requirements of real-time monitoring. This method enables automatic identification and warning of behaviours such as wearing safety equipment and intrusion into dangerous areas, providing effective technical support for building an intelligent safety defence line.

Keywords: construction site safety monitoring; target detection; YOLOv8; real-time system.

DOI: 10.1504/IJRIS.2026.153455

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.12, pp.66 - 82

Received: 30 Jan 2026
Accepted: 03 Mar 2026
Published online: 08 May 2026 *

Title: Object detection for construction site safety monitoring based on Yolov8 model

Keep up-to-date